genoplot: Genome plot of the eQTL data on the expression traits...
In eqtl: Tools for analyzing eQTL experiments: A complementary to Karl Broman's 'qtl' package for genome-wide analysis
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
Plots the estimated eQTL positions with the genomic positions of the controlled gene.
Usage
1
Arguments
cross
An object of class cross. See 'qtl' package manual for read.cross function details.
peak.array
An object of class peak.array. See peak.2.array function for details.
etrait.coord
A data frame specifying the etrait genomic locations with columns:
etrait.name a factor with array element or gene name as levels.
chr an integer vector determining the chromosome.
start an integer vector determining the GST start location in base pair.
stop an integer vector determining the GST stop location in base pair.
data.gmap
A data frame with column names "Marker", "chr" and "PP" specifying the marker physical locations. Those one must be the same markers defined in the related cross object.
data.gmap\$Marker is a vector character strings specifying the names of markers.
data.gmap\$chr is a vector of integers specifying the chromosome on which the markers are localized.
data.gmap\$PP is a vector of integers specifying the physical marker location on the chromosome in base pair.
chr.size
A vector of integer specifying the size of the chromosomes in base pair in order of the chromosomes.
save.pict
If TRUE, save each charts generated by genoplot as png files in the current folder.
...
Ignored at this step.
Details
Useful for genetical genomics studies. This function gives a graphical overview of the global eQTL network by plotting the estimated eQTL positions with the genomic positions of the affected traits. Six charts are generated and all locations data are represented on a physical scale. The genomic ditribution of both affected traits and QTLs are described by two histograms. If save.pict=TRUE, these histograms are saved as ‘./histogram\_controled\_gst.png’ and ‘./histogram\_qtl.png’ files, respectively. The etrait~eQTL plot are represented with LOD color scale (from green to red in order of increasing LOD score, blue representing the average LOD SCORE) and with additive effect color scale (from green to red in order of increasing additive effect, yellow representing the null additive effect). Four etrait~eQTL plot are generated representing the eQTL locations as single LOD peaks or support interval regions, both with LOD and additive effect color scales. If save.pict=TRUE, these plot are saved as ‘lod\_dotplot\_traitxqtl.png’, ‘ae\_dotplot\_traitxqtl.png’, ‘lod\_siplot\_traitxqtl.png’ and ‘ae\_siplot\_traitxqtl.png’ files.
Value
return a list with elements:
coord_etrait
the etrait coordinates.
coord_qtl
the QTL coordinates.
limit
the chromosomes limits.
add_etrait
the cumulates size of the chromosomes in bp for the etrait.
add_qtl
the cumulates size of of the chromosomes in bp for the QTL.
Author(s)
Hamid A. Khalili
See Also
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
data(seed10);
seed10 <- calc.genoprob( cross=seed10, step=2, off.end=0, error.prob=0,
map.function='kosambi', stepwidth='fixed');
seed10 <- sim.geno( cross=seed10, step=2, off.end=0, error.prob=0,
map.function='kosambi', stepwidth='fixed');
out.em <- scanone( seed10, pheno.col=1:50, model='normal', method='hk');
out.peak <- define.peak( out.em, 'all');
out.peak <- calc.adef(seed10,out.em,out.peak);
data(BSpgmap);
data(ATH.coord);
out.peak <- localize.qtl(seed10, out.peak, BSpgmap);
out.array <- peak.2.array(out.peak)
genoplot(out.array, seed10, ATH.coord, BSpgmap,
chr.size=c(30432457,19704536,23470536,18584924,26991304), save.pict=TRUE);
# NB: the size of the Arabidopsis thaliana chromosomes are
# 30432457, 19704536, 23470536, 18584924 and 26991304 total base pairs
# for chromosomes 1 to 5 respectively
Example output
Loading required package: qtl
no. of traits: 50
trait: ID lodcolumn: 1
trait: CATcmt lodcolumn: 2
trait: CATmjm lodcolumn: 3
trait: CATppt lodcolumn: 4
trait: CATvvr lodcolumn: 5
trait: CATxni lodcolumn: 6
trait: CATfgb lodcolumn: 7
trait: CATqoq lodcolumn: 8
trait: CATsgu lodcolumn: 9
trait: CATwwh lodcolumn: 10
trait: CATpxm lodcolumn: 11
trait: CATkks lodcolumn: 12
trait: CATbaa lodcolumn: 13
trait: CATham lodcolumn: 14
trait: MITO312 lodcolumn: 15
trait: CATudg lodcolumn: 16
trait: CATwgo lodcolumn: 17
trait: CATdbn lodcolumn: 18
trait: CATipy lodcolumn: 19
trait: CATpyn lodcolumn: 20
trait: CATfnu lodcolumn: 21
trait: CATjoj lodcolumn: 22
trait: CATpsl lodcolumn: 23
trait: CATntj lodcolumn: 24
trait: CATjwf lodcolumn: 25
trait: CATrck lodcolumn: 26
trait: CATane lodcolumn: 27
trait: CAToti lodcolumn: 28
trait: CATbzx lodcolumn: 29
trait: CATrjo lodcolumn: 30
trait: CATjxh lodcolumn: 31
trait: CATlkr lodcolumn: 32
trait: CATnrd lodcolumn: 33
trait: CATtez lodcolumn: 34
trait: CATjpz lodcolumn: 35
trait: CATbko lodcolumn: 36
trait: CATavz lodcolumn: 37
trait: CATvkm lodcolumn: 38
trait: CATuhl lodcolumn: 39
trait: CATlbg lodcolumn: 40
trait: CATvbb lodcolumn: 41
trait: CATowo lodcolumn: 42
trait: CATlzh lodcolumn: 43
trait: CATepg lodcolumn: 44
trait: CATbuu lodcolumn: 45
trait: CATwrn lodcolumn: 46
trait: CATtgk lodcolumn: 47
trait: CATxmc lodcolumn: 48
trait: CATdmq lodcolumn: 49
trait: CATbzp lodcolumn: 50
define.peak in process...
peaks features:
[1] "lod" "mname.peak" "peak.cM" "mname.inf"
[5] "inf.cM" "mname.sup" "sup.cM" "si.quality"
[9] "additive.effect" "peak.bp" "inf.bp" "sup.bp"
[1] "chromosome size:"
[1] 0.00 30432.46 19704.54 23470.54 18584.92 26991.30
[1] "cumul:"
[1] 0.00 30432.46 50136.99 73607.53 92192.45 119183.76
26.89773 2.314713
1.00261 -1.187079
$coord_gst
[1] 8720.609 8720.609 8720.609 942.187 1475.018 1475.018 11774.705
[8] 21928.811 21928.811 21928.811 20896.379 3789.822 6021.201 6021.201
[15] 286.199 286.199 10351.938 10351.938 935.942 935.942 13494.665
[22] 29290.554 25921.385 25921.385 6858.294 7099.383 16308.151 16439.598
[29] 21203.945 2171.654 19067.570 19067.570 9668.429 24358.439 13547.744
[36] 19701.814 11487.254 16732.502 19252.182 16102.782 16102.782 16102.782
[43] 17379.638 8863.137 8863.137 12686.618 12686.618 1227.614 8200.967
[50] 8200.967 8200.967 8200.967 29091.044 20629.801 20629.801 19787.339
[57] 17884.051
$coord_qtl
[1] 89.4980 905.5649 699.9895 23024.2033 1404.1199 1507.1040
[7] 905.5649 407.0100 22442.8003 24884.7764 905.5649 905.5649
[13] 905.5649 1036.2872 89.4980 1404.1199 905.5649 1036.2872
[19] 12810.5181 18406.0660 1404.1199 1404.1199 905.5649 1036.2872
[25] 6905.9975 1404.1199 905.5649 1404.1199 905.5649 1036.2872
[31] 89.4980 1404.1199 905.5649 24374.0080 905.5649 905.5649
[37] 905.5649 905.5649 905.5649 89.4980 905.5649 699.9895
[43] 89.4980 89.4980 905.5649 89.4980 905.5649 905.5649
[49] 8192.9510 905.5649 7900.2219 6036.5479 2401.2298 9570.1792
[55] 14276.4258 905.5649 407.0100
$limit
[1] 0.00 30432.46 50136.99 73607.53 92192.45 119183.76
$add_gst
[1] 73607.53 73607.53 73607.53 0.00 50136.99 50136.99 0.00 92192.45
[9] 92192.45 92192.45 92192.45 92192.45 73607.53 73607.53 0.00 0.00
[17] 30432.46 30432.46 30432.46 30432.46 50136.99 0.00 92192.45 92192.45
[25] 30432.46 50136.99 92192.45 0.00 92192.45 0.00 92192.45 92192.45
[33] 92192.45 0.00 92192.45 92192.45 73607.53 0.00 50136.99 73607.53
[41] 73607.53 73607.53 73607.53 50136.99 50136.99 73607.53 73607.53 73607.53
[49] 0.00 0.00 0.00 0.00 0.00 92192.45 92192.45 92192.45
[57] 0.00
$add_qtl
[1] 73607.53 73607.53 92192.45 92192.45 73607.53 92192.45 73607.53 73607.53
[9] 92192.45 92192.45 73607.53 73607.53 73607.53 92192.45 73607.53 73607.53
[17] 73607.53 92192.45 0.00 50136.99 73607.53 73607.53 73607.53 92192.45
[25] 30432.46 73607.53 73607.53 73607.53 73607.53 92192.45 73607.53 73607.53
[33] 73607.53 0.00 73607.53 73607.53 73607.53 73607.53 73607.53 73607.53
[41] 73607.53 92192.45 73607.53 73607.53 73607.53 73607.53 73607.53 73607.53
[49] 0.00 73607.53 73607.53 92192.45 73607.53 30432.46 30432.46 73607.53
[57] 73607.53
eqtl documentation built on May 2, 2019, 5:42 a.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
Plots the estimated eQTL positions with the genomic positions of the controlled gene.
Usage
1 |
Arguments
cross |
An object of class |
peak.array |
An object of class |
etrait.coord |
A data frame specifying the etrait genomic locations with columns: |
data.gmap |
A data frame with column names |
chr.size |
A vector of integer specifying the size of the chromosomes in base pair in order of the chromosomes. |
save.pict |
If TRUE, save each charts generated by |
... |
Ignored at this step. |
Details
Useful for genetical genomics studies. This function gives a graphical overview of the global eQTL network by plotting the estimated eQTL positions with the genomic positions of the affected traits. Six charts are generated and all locations data are represented on a physical scale. The genomic ditribution of both affected traits and QTLs are described by two histograms. If save.pict=TRUE, these histograms are saved as ‘./histogram\_controled\_gst.png’ and ‘./histogram\_qtl.png’ files, respectively. The etrait~eQTL plot are represented with LOD color scale (from green to red in order of increasing LOD score, blue representing the average LOD SCORE) and with additive effect color scale (from green to red in order of increasing additive effect, yellow representing the null additive effect). Four etrait~eQTL plot are generated representing the eQTL locations as single LOD peaks or support interval regions, both with LOD and additive effect color scales. If save.pict=TRUE, these plot are saved as ‘lod\_dotplot\_traitxqtl.png’, ‘ae\_dotplot\_traitxqtl.png’, ‘lod\_siplot\_traitxqtl.png’ and ‘ae\_siplot\_traitxqtl.png’ files.
Value
return a list with elements:
coord_etrait |
the etrait coordinates. |
coord_qtl |
the QTL coordinates. |
limit |
the chromosomes limits. |
add_etrait |
the cumulates size of the chromosomes in bp for the etrait. |
add_qtl |
the cumulates size of of the chromosomes in bp for the QTL. |
Author(s)
Hamid A. Khalili
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | data(seed10);
seed10 <- calc.genoprob( cross=seed10, step=2, off.end=0, error.prob=0,
map.function='kosambi', stepwidth='fixed');
seed10 <- sim.geno( cross=seed10, step=2, off.end=0, error.prob=0,
map.function='kosambi', stepwidth='fixed');
out.em <- scanone( seed10, pheno.col=1:50, model='normal', method='hk');
out.peak <- define.peak( out.em, 'all');
out.peak <- calc.adef(seed10,out.em,out.peak);
data(BSpgmap);
data(ATH.coord);
out.peak <- localize.qtl(seed10, out.peak, BSpgmap);
out.array <- peak.2.array(out.peak)
genoplot(out.array, seed10, ATH.coord, BSpgmap,
chr.size=c(30432457,19704536,23470536,18584924,26991304), save.pict=TRUE);
# NB: the size of the Arabidopsis thaliana chromosomes are
# 30432457, 19704536, 23470536, 18584924 and 26991304 total base pairs
# for chromosomes 1 to 5 respectively
|
Example output
Loading required package: qtl
no. of traits: 50
trait: ID lodcolumn: 1
trait: CATcmt lodcolumn: 2
trait: CATmjm lodcolumn: 3
trait: CATppt lodcolumn: 4
trait: CATvvr lodcolumn: 5
trait: CATxni lodcolumn: 6
trait: CATfgb lodcolumn: 7
trait: CATqoq lodcolumn: 8
trait: CATsgu lodcolumn: 9
trait: CATwwh lodcolumn: 10
trait: CATpxm lodcolumn: 11
trait: CATkks lodcolumn: 12
trait: CATbaa lodcolumn: 13
trait: CATham lodcolumn: 14
trait: MITO312 lodcolumn: 15
trait: CATudg lodcolumn: 16
trait: CATwgo lodcolumn: 17
trait: CATdbn lodcolumn: 18
trait: CATipy lodcolumn: 19
trait: CATpyn lodcolumn: 20
trait: CATfnu lodcolumn: 21
trait: CATjoj lodcolumn: 22
trait: CATpsl lodcolumn: 23
trait: CATntj lodcolumn: 24
trait: CATjwf lodcolumn: 25
trait: CATrck lodcolumn: 26
trait: CATane lodcolumn: 27
trait: CAToti lodcolumn: 28
trait: CATbzx lodcolumn: 29
trait: CATrjo lodcolumn: 30
trait: CATjxh lodcolumn: 31
trait: CATlkr lodcolumn: 32
trait: CATnrd lodcolumn: 33
trait: CATtez lodcolumn: 34
trait: CATjpz lodcolumn: 35
trait: CATbko lodcolumn: 36
trait: CATavz lodcolumn: 37
trait: CATvkm lodcolumn: 38
trait: CATuhl lodcolumn: 39
trait: CATlbg lodcolumn: 40
trait: CATvbb lodcolumn: 41
trait: CATowo lodcolumn: 42
trait: CATlzh lodcolumn: 43
trait: CATepg lodcolumn: 44
trait: CATbuu lodcolumn: 45
trait: CATwrn lodcolumn: 46
trait: CATtgk lodcolumn: 47
trait: CATxmc lodcolumn: 48
trait: CATdmq lodcolumn: 49
trait: CATbzp lodcolumn: 50
define.peak in process...
peaks features:
[1] "lod" "mname.peak" "peak.cM" "mname.inf"
[5] "inf.cM" "mname.sup" "sup.cM" "si.quality"
[9] "additive.effect" "peak.bp" "inf.bp" "sup.bp"
[1] "chromosome size:"
[1] 0.00 30432.46 19704.54 23470.54 18584.92 26991.30
[1] "cumul:"
[1] 0.00 30432.46 50136.99 73607.53 92192.45 119183.76
26.89773 2.314713
1.00261 -1.187079
$coord_gst
[1] 8720.609 8720.609 8720.609 942.187 1475.018 1475.018 11774.705
[8] 21928.811 21928.811 21928.811 20896.379 3789.822 6021.201 6021.201
[15] 286.199 286.199 10351.938 10351.938 935.942 935.942 13494.665
[22] 29290.554 25921.385 25921.385 6858.294 7099.383 16308.151 16439.598
[29] 21203.945 2171.654 19067.570 19067.570 9668.429 24358.439 13547.744
[36] 19701.814 11487.254 16732.502 19252.182 16102.782 16102.782 16102.782
[43] 17379.638 8863.137 8863.137 12686.618 12686.618 1227.614 8200.967
[50] 8200.967 8200.967 8200.967 29091.044 20629.801 20629.801 19787.339
[57] 17884.051
$coord_qtl
[1] 89.4980 905.5649 699.9895 23024.2033 1404.1199 1507.1040
[7] 905.5649 407.0100 22442.8003 24884.7764 905.5649 905.5649
[13] 905.5649 1036.2872 89.4980 1404.1199 905.5649 1036.2872
[19] 12810.5181 18406.0660 1404.1199 1404.1199 905.5649 1036.2872
[25] 6905.9975 1404.1199 905.5649 1404.1199 905.5649 1036.2872
[31] 89.4980 1404.1199 905.5649 24374.0080 905.5649 905.5649
[37] 905.5649 905.5649 905.5649 89.4980 905.5649 699.9895
[43] 89.4980 89.4980 905.5649 89.4980 905.5649 905.5649
[49] 8192.9510 905.5649 7900.2219 6036.5479 2401.2298 9570.1792
[55] 14276.4258 905.5649 407.0100
$limit
[1] 0.00 30432.46 50136.99 73607.53 92192.45 119183.76
$add_gst
[1] 73607.53 73607.53 73607.53 0.00 50136.99 50136.99 0.00 92192.45
[9] 92192.45 92192.45 92192.45 92192.45 73607.53 73607.53 0.00 0.00
[17] 30432.46 30432.46 30432.46 30432.46 50136.99 0.00 92192.45 92192.45
[25] 30432.46 50136.99 92192.45 0.00 92192.45 0.00 92192.45 92192.45
[33] 92192.45 0.00 92192.45 92192.45 73607.53 0.00 50136.99 73607.53
[41] 73607.53 73607.53 73607.53 50136.99 50136.99 73607.53 73607.53 73607.53
[49] 0.00 0.00 0.00 0.00 0.00 92192.45 92192.45 92192.45
[57] 0.00
$add_qtl
[1] 73607.53 73607.53 92192.45 92192.45 73607.53 92192.45 73607.53 73607.53
[9] 92192.45 92192.45 73607.53 73607.53 73607.53 92192.45 73607.53 73607.53
[17] 73607.53 92192.45 0.00 50136.99 73607.53 73607.53 73607.53 92192.45
[25] 30432.46 73607.53 73607.53 73607.53 73607.53 92192.45 73607.53 73607.53
[33] 73607.53 0.00 73607.53 73607.53 73607.53 73607.53 73607.53 73607.53
[41] 73607.53 92192.45 73607.53 73607.53 73607.53 73607.53 73607.53 73607.53
[49] 0.00 73607.53 73607.53 92192.45 73607.53 30432.46 30432.46 73607.53
[57] 73607.53
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